Written by Jakub Rusinowski · Last updated September 19, 2026
The 8 GB 4060 Ti. Worth distinguishing from the 16 GB card, which shares the same 288 GB/s but holds twice the model — for local inference the capacity is usually the deciding number, not the speed.
| VRAM | 8 GB |
| Memory Bandwidth | 288 GB/s |
| TDP | 160 W |
| Architecture | Ada Lovelace AD106 |
| Release Year | 2023 |
| MSRP at Launch | $399 |
| Inference Speed (Llama 3.1 8B Q4_K_M) | 31–59 tok/s (estimated) |
| Inference Speed (Llama 3.3 70B Q4_K_M) | Does not fit — needs ~44 GB of 8 GB usable |
or compare on Vast.ai from $0.35/hr (typical low · varies)
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All models below run comfortably in 8 GB VRAM with Q4_K_M quantization.
| Bielik | Bielik PL 11B v3.0 Instruct · 7 GB VRAM · Q4_K_M · bielik |
| Llama 3.2 Family | Llama 3.2 11B Vision Instruct · 7 GB VRAM · Q4_K_M · llama-3-2 |
| Llama 3.2 Vision | Llama 3.2 Vision 11B · 7 GB VRAM · Q4_K_M · ollama run llama3.2-vision:11b |
| Falcon 3 | Falcon 3 10B Instruct · 7 GB VRAM · Q4_K_M · ollama run falcon3:10b |
| Gemma 2 Family | Gemma 2 9B IT · 6 GB VRAM · Q4_K_M · ollama run gemma2 |
| GLM-4.7 / GLM-Z1 | GLM-4 9B · 6 GB VRAM · Q4_K_M · ollama run glm4:9b |
| Qwen 3.5 | Qwen 3.5 9B · 6 GB VRAM · Q4_K_M · ollama run qwen3.5:9b |
| GLM-4.6V | GLM-4.6V-Flash 9B · 6 GB VRAM · Q4_K_M · glm-4-6v |
34 more families also fit 8 GB — browse the full model library.
Yes — the NVIDIA GeForce RTX 4060 Ti 8GB has 8 GB VRAM and runs The 8 GB 4060 Ti. Worth distinguishing from the 16 GB card, which shares the same 288 GB/s but holds twice the model — f
The NVIDIA GeForce RTX 4060 Ti 8GB is estimated to run Llama 3.1 8B at 31–59 tok/s with Q4_K_M quantization. Llama 3.3 70B does not fit: it needs about 44 GB against 8 GB usable. These are modelled estimates, not measurements — see /en/methodology.
With 8 GB you can run: Bielik, Llama 3.2 Family, Llama 3.2 Vision, Falcon 3, Gemma 2 Family. Use Ollama for the easiest setup: ollama run llama3.1:8b.
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